Learning Analytics Can Make Your Life Easier

Feb 5, 2016

4 Min Read

Learning analytics is a hot topic in edtech right now and it’s no wonder, with schools reporting that they’re able to predict student outcomes to within a letter grade 67% of the time*, as early as the fourth week of classes!

So what is learning analytics all about? Why is it so important and why should you care? Well for one thing, if you teach or have anything to do with the business of teaching, it can make your life a heck of a lot easier. Unfortunately, all too often when a conversation about analytics takes place, it’s done from the perspective of technology and the systems and processes that are needed to evaluate huge quantities of data. This is where the concept of learning analytics seems difficult to grasp.

So let me clear up one thing – analytics does not equal data. Learning analytics is simply about using data to understand and improve learning experiences and outcomes. It’s about using data to give humans KNOWLEDGE. Knowledge that they can ACT on (and do it at scale) so that they can influence RESULTS.

We do this every day, don’t we? We use the data (i.e. the information in our environment) to make decisions on how to act to get the results we want. So let’s talk about how learning analytics can make your life easier.

How does learning analytics work in education?

The environment in this case, is the classroom, whether it be online or brick-and-mortar. What is measured are the interactions and behaviours exhibited by students in these environments. The desired result is to improve the outcomes for the learners in that environment.

Why should I care?

Here’s where learning analytics can be powerful. It can help educators be smarter about how they interact with their students. This is important now more so than ever, because the pressures institutions face are mounting. For one thing, budgets are being reduced, forcing people to have to do more with less. Investing in technology can be helpful here, as it can save you money in the long-term by automating tasks and pointing out where time and energy should be spent. Student populations are also changing. The number of non-traditional learners entering higher education is growing, and a new generation of learners expect their technology to be smart and personalized.

Can you give me a real-world example?

Here’s an example I see all the time. Imagine an instructor, let’s call her Ms. Bright, who’s looking at how the students in her course performed. The result she wants to achieve is for more of her students to complete the course. The instructor can see that the same students who performed well were also active in discussions, before they submitted the first high-stakes assignment of the course. With this KNOWLEDGE in hand, the instructor can then choose to ACT.

When Ms. Bright recognizes the assignment submission date looming, she can proactively reach out to those who are not participating in discussions, and motivate them to be more engaged. The RESULT is that students get some personal attention, clarity on successful behaviours, and can correct their behaviour before it’s too late.

Georgia Southern University is also a great example of a school using learning data to improve retention and graduation rates. Watch their webinar to see how they did it.

What about scalability? How can I do this for a larger class?

This is where your investment in technology pays off. Creating this level of individual interaction is extremely difficult and impractical without having a way to automate and simplify the process. The right system can help automate tasks, quickly identify at-risk students, and free up the instructors’ time to provide a deeper level of personalized teaching.

Of course learning analytics has its difficulties, depending on what you want to get out of it. There is a lot of science and rigor that goes into bringing data together from many different systems, making sense of it, and then actually DOING something with it. The possibilities here are endless, from predicting which students are at risk of dropping a course or failing, to predicting which courses a student should take to complete their degree on time, to which courses will provide the greatest chance of success, to even helping recommend learning goals based on desired careers. But I’ll save the geeking out on the world of possibilities here for another post.

For now, when you’re thinking about learning analytics, start from what you want to achieve and work backwards. Maybe you don’t know and just want to measure what you’re doing – that’s a great starting point too! With your outcomes as your true north, you can evaluate how you want to measure, what your specific targets are, what data you’ll need, and who will need to act on it.

As I mentioned before, Georgia Southern University is a great example of an institution that used learner data to identify at-risk students, and intervened before it was too late. Have a look at their webinar to learn how they did it.